4 steps for solution architects to overcome RPA challenges

Long known for its potential to streamline business practices and reduce costs, robotic process automation is now one of the big buzzwords.

Prominent organizations such as Ernst & Young, Walmart and American Express are implementing the software, and today, its popularity is at an all-time high. A recent survey of 400 executives found that 53 percent of respondents have already initiated efforts to embed RPA, and in two years, this number is expected to rocket up to 72 percent. This doesn’t come as too much of a surprise though, as RPA’s benefits are clear.

Simply defined, RPA is the use of robotic software to automate time-consuming, high-volume, repetitive back-office activities. This advanced processing allows for less human interaction, enhanced data quality and more productivity and better business collaboration – all key benefits for data-intensive organizations.

Common applications of this technology include data migration between applications and data extraction from bank statements for record reconciliation. Solutions architects, who are responsible for this technology implementation, see great value as it gets their “data house” in order.

Here are four trade secrets to their success:

1. Debunk myths and false perceptions

Whenever firms look to implement new technology into their infrastructure, there can be an initial misperception that the solution will solve more problems than originally intended to. This is no different with RPA technology. RPA is a powerful innovation, but it’s not an all-encompassing data solution.

One of the most important jobs of a solution architect is to explain the technology to executives, cut through the buzzwords, and be clear and realistic about RPA’s possibilities and limitations upfront.

To achieve clear communication lines, solution architects must incorporate key stakeholders in the implementation. This includes inviting everyone from business executives, to the developers, to the RPA users (i.e. HR professionals, customer service reps, etc.) in initial discussions.

Not only does this remove possibilities of dashed hopes or false truths, but it also provides an opportunity for solution architects to get a comprehensive understanding of the company, insight into the personalized needs of each department and executive and avoid data silos stemming from a lack of diverse voices. RPA can create a unified data management platform that empowers everyone to be involved with data creation and analysis. It may also uncover areas of overlap where RPA technology can drive greater collaboration and data set sharing across departments.

2. Relieve fears that RPA will replace workers

As important as it is for solution architects to talk about the technology, it’s equally crucial that they listen to concerns too. Especially when discussing automation – or even just mentioning the word robotics, architects must realize it’s natural for some groups to have anxiety about being replaced or their positions eliminated. This belief often stems from a lack of education about the technology.

In times like these, solution architects need to understand where clients are coming from and leverage the questions as opportunities to communicate the real benefit of RPA: which is to make employee lives easier by removing tedious tasks like data entry that most workers don’t enjoy. Managers also need to know that this level of automation will likely result in greater trust in the accuracy of the data, which is something that can’t be easily assessed when humans are doing the data extraction and entry.

Overcoming executives’ fears about job security is vital to a successful implementation, because with better client education, comes more comfort with RPA, which leads to increased commitment to using the technology, and ultimately a successful RPA implementation.

3. Foster a culture of collaboration

A spirit of teamwork and partnership between the executives, department heads and business users and the solution architect is important. As solution architects know, RPA is not a fault-tolerant technology. Given that each client and company has personalized needs and unique data inputs, it’s important that solutions architects explain that an RPA implementation is an iterative process.

Solutions architects must describe that though processes can be automated for structured inputs using rule-based logic, much of the RPA implementation is dedicated to seeking outliers that don’t conform to the general algorithm. Said another way, this means that a decision to utilize RPA is a commitment to long-term partnership.

We’ve all heard the phrase “garbage in, garbage out,” and its meaning certainly applies to RPA. Before clients can maximize the potential cost/time savings inherent in today’s modern RPA solutions, they must first ensure the data sets they’re passing to bots are clean, accurate and complete.

Encourage executives to look for data preparation solutions that can work with the RPA solution to extract data from any source, convert unstructured data into rows and columns and clean data with pre-built functions that can be automated with reusable data workspaces or models. You’ll also want to be sure that the solution you invest in has advanced export features, including custom connectors and APIs that can autofill the UI of your chosen RPA solution.

And the beauty is that once the data cleansing and export is set up, it can be fully automated. Your digital workforce is fully enabled and ready to produce. The combination of automated data preparation and RPA frees up employees to focus on other tasks as well as build collaborative teams for the creation of new data models, predictions and business outcomes.

RPA has incredible potential to streamline processes, increase productivity and cut labor costs, but those benefits should not be underscored at the expense of truth and realistic expectations. As important as it is to help clients roll out RPA technology, its equally crucial that solution architects educate clients, debunk myths, create a culture of collaboration and advise on proper data preparation beforehand. It is only from this strong foundation that a successful RPA implementation can be laid.